The technology is never the hard part.
I'm a systems thinker who builds the AI strategy enterprise teams keep using. Tools, plans, and the metrics that prove it worked.
What are you hiring for?
One tap. Nothing is saved.
Outcomes, measured in people.
Every piece of work I write up the same way: the situation, what I changed, and the number that proved it. Not licenses sold or hours billed. Whether the people expected to use the thing used it, and whether the risk went down. Two from Google, in that format. The numbers are mine, from the data at the time.
ATLAS: nobody learns about a Sev-1 from the customer
- Situation
- Enterprise accounts where the queue was the risk. Manual queue-checking, scattered tabs, and customers telling us something was wrong before we knew.
- What I did
- Built ATLAS: Salesforce case data into BigQuery, SQL monitors that fire the second an SLA threshold is breached, Vertex AI to say why a case stalled and who holds the next move, and a morning brief and live alerts in Slack.
- Result
- More than 30% fewer formal customer escalations, sustained. Still running at Google as of my last day. Rebuilt here as a browser tool you can load your own queue into.
Mandatory GenAI training, unblocked for 300+ colleagues
- Situation
- A mandatory GenAI badge program: 30 to 40 hours of timed, Python-based labs whose directions didn't work as written. Hundreds of technical and non-technical colleagues stalled against a fixed deadline.
- What I did
- Wrote the step-by-step guide with the working fix for every broken lab step, shared it across Google Cloud, then built a Gemini agent carrying the same notes so people got unblocked without waiting for me, including while I was on vacation.
- Result
- Adopted by more than 300 people. Completion time down from 30 to 40 hours to roughly 10. Recognition from the VP and Global Head of Professional Services and the global program lead for AI/ML technical enablement.
When AI comes up at work, most people feel…
Do staff have a real say in how AI is used in their work?
Staff trust scored 1 on at least one question. Address fear before anything else.
Before any tool, hold an open session on what AI will not replace, and invite the people doing the work to help shape the pilot.
Measures use, not licenses.
The Adoption Tracker mirrors the fields of the Claude Enterprise Analytics API and shows which teams adopted and which need support. Sample data, fictional org.
Pilot to production, written down.
A 90-day plan to take a 15-agent pilot to all 220 agents of a customer care team. Discovery questions, a stakeholder map, an operating model, six governance gates, fourteen champions, and the metrics that decide scale, fix or stop.
See the 90-day planBuilt in the open.
Working projects that already run but are still moving. The questions change, the weights change, the copy changes. Use them anyway, and tell me where they break.
AI Security & Scaling Readiness Scorer
Is your security setup ready for AI, and for the growth AI is about to cause? Sixteen questions drawn from NIST CSF 2.0 and the CIS Controls v8.1, industry overlays for healthcare, finance and government in the US, the EU or both, and a platform check for Claude or Gemini. Other industries are not scored yet.
Human first, AI second.
Listen
Map the people, the incentives and the fear before touching a tool. The people who will use it get the most airtime.
Score
Measure readiness honestly, with the team in the room. Staff trust gates the result. A high score with low trust means slow down.
Pilot
One use case, a baseline, a timeline, a named owner. Watch the signals weekly. Stop is an acceptable answer, and saying so early is why people trust the rollout.
Hand over
Leave the team owning the policy, the metrics and the next wave. The guardrails stay in place after I leave.
An agent that does the quiet work.
One agent, my Chief of Staff, running on Claude under five rules. It drafts, checks and flags. I decide and I publish.
What I'm reading today.
Picked each morning by my Chief of Staff, my agent. Read by me.
A short list of AI stories each day, chosen for people who run adoption, governance and customer work, not for people who build models. The agent scans the feeds, traces every claim back to its original source, counts twelve outlets repeating one report as one source, and flags anything the sources disagree on for me to decide. Each headline links to the original reporting. It's where most of my posts start.
Writing
All posts on LinkedIn
I build the strategy, then I stay for the implementation.
I’m a technology leader and AI strategist with more than a decade of experience across Google, Microsoft, VMware, and Dell.
I work at the intersection of AI transformation, enterprise technology, security, governance, and large-scale deployment, helping organizations move from strategy to systems that work in the real world.
I’ve worked with Fortune 100 and Fortune 500 companies, supporting complex enterprise environments and large-scale customer and partner initiatives. At one point, I managed a $1.6 billion account portfolio.
My work sits across the full path:
Strategy → Architecture → Implementation → Security → Governance → Adoption → Scale
With AI, that becomes:
AI opportunity → Workflow → Design → Build → Test → Deploy → Operate → Measure → Improve
I’m interested in what happens after the demo.
- How does AI actually get deployed?
- How does it connect to data, tools, workflows, and controls?
- How do organizations make it secure and governable?
- How do people actually use it?
- How do you know it is creating value?
- And how do you build systems people can rely on every day?
I’ve also spent years mentoring startups, founders, nonprofits, and emerging technology teams, helping turn technical ideas into practical strategies and execution plans.
I’m the founder of Black Women in the Age of AI (BWAI Studio), where my focus expands from enterprise implementation to a larger question:
How do we make sure people have the knowledge, capability, representation, and agency to shape the future of AI?
This site is a portfolio of that work.
Technology is only useful when it works in the real world.
The longer versionPost-sales, after the contract is signed. Principal, senior and founding roles in AI transformation, strategy, deployment, implementation and governance, and in customer and partner success.